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Field
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performance utilizing reliable and computationally efficient numerical structural models. To support the condition (state) assessment, you will also explore the use of advanced estimators (e.g., Kalman Filter
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of the nonlinear structural performance utilizing reliable and computationally efficient numerical structural models. To support the condition (state) assessment, you will also explore the use of advanced estimators
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The research project focuses on advancing seismic resilience strategies for industrial facilities by integrating and validating base isolation techniques. The doctoral candidate will numerically and
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nonlinear effects. These nonlinear effects will be generalised via correction terms discovered by machine learning from a large numerical simulated dataset. This dataset also allows for extending the theory
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in thermodynamics, optimization, and control theory. Strong understanding of mathematical modeling, numerical optimization, and/or model predictive control (MPC). Experience working with large-scale
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-energy impact events using LS-DYNA and other applicable numerical analysis methods/computer simulation codes. Prepare and submit proposals/budgets to acquire new funding. Meet with prospective clients
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than conventional structures. Superstructure optimization, which uses a predefined architecture with numerous unit operations and possible paths, offers an alternative for identifying non-intuitive
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computationally efficient numerical structural models. To support the condition (state) assessment, the project will also explore the use of advanced estimators (e.g., Kalman Filter) or Machine Learning models
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efforts to contribute to safer marine operations, we actively explore possibilities to utilize both numerical and machine learning methods to enhance the accuracy and resolution of metocean forecasts. About
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analysis, UV-Vis and fluorescence spectroscopy, nitrogen adsorption–desorption isotherms at 77 K, thermogravimetry (TGA), perfilometry and electrical measurements, also including knowledge in data